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Primary Production

Primary Production

The total amount of energy acquired by primary producers in an ecosystem is called gross primary production (GPP). However, of this energy, producers use some for metabolic processes, and some is lost as heat, decreasing the amount of energy available to the next trophic level. The remaining usable amount of energy is called the net primary productivity (NPP). In terrestrial ecosystems, NPP is driven by climate, while light penetration and nutrient availability drive NPP in aquatic ecosystems.

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Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

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Scaling up benthic primary productivity estimates in a large intertidal estuary using remote sensing.

Zhanchao Shao¹, Karin R Bryan¹, Moritz K Lehmann²

¹School of Science, University of Waikato, Hamilton 3260, New Zealand.

The Science of the Total Environment

|September 28, 2023

Summary

Seagrass and microphytobenthos (MPB) are key estuarine producers. A new machine learning model using Sentinel-2 data accurately maps these habitats and estimates their gross primary productivity (GPP), revealing sensitivity to sea level rise and turbidity.

Keywords:

Gross primary productivity Machine learning Microphytobenthos Sea level rise Seagrass Sentinel-2

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Area of Science:

Marine ecology
Remote sensing
Ecosystem modeling

Background:

Seagrass and microphytobenthos (MPB) are crucial primary producers in temperate intertidal zones, supporting vital ecosystem functions.
Effective monitoring of estuarine productivity is essential for understanding ecosystem responses to environmental stressors like climate change and pollution.

Purpose of the Study:

To develop and apply a novel machine learning model using Sentinel-2 data for estimating estuary-wide gross primary productivity (GPP) of seagrass and MPB.
To assess the accuracy of mapping seagrass and unvegetated habitats and predicting seagrass coverage.

Main Methods:

Utilized supervised classification (Random Forest) for habitat delineation and Artificial Neural Network (ANN) regression for seagrass coverage prediction.
Coupled habitat mapping with literature-derived photosynthesis-irradiance (P-I) relationships to estimate GPP.
Validated model accuracy with high scores for both classification (0.96 overall accuracy) and regression (R² = 0.71, RMSE = 0.11).

Main Results:

The model accurately delineated seagrass and unvegetated areas and predicted seagrass coverage.
Seagrass was found to contribute slightly more to intertidal benthic productivity than MPB in the study estuary over three years.
Projected future GPP under sea level rise scenarios indicated significant declines, particularly under current turbidity trends (up to 53% loss for seagrass, 45% for MPB).

Conclusions:

The developed model provides a robust method for large-scale estimation of estuarine GPP.
Future estuarine productivity is highly sensitive to sea level rise, with increased turbidity exacerbating negative impacts on seagrass and MPB.
Controlling water turbidity is identified as a key strategy for maintaining estuarine productivity in the face of rising sea levels.